Abstract: An overview of the important aspects of managing
and controlling industrial effluent discharges to public sewers namely
sampling, characterization, quantification and legislative controls has
been presented. The findings have been validated by means of a case
study covering three industrial sectors namely, tanning, textile
finishing and food processing industries. Industrial effluents
discharges were found to be best monitored by systematic and
automatic sampling and quantified using water meter readings
corrected for evaporative and consumptive losses. Based on the
treatment processes employed in the public owned treatment works
and the chemical oxygen demand and biochemical oxygen demand
levels obtained, the effluent from all the three industrial sectors
studied were found to lie in the toxic zone. Thus, physico-chemical
treatment of these effluents is required to bring them into the
biodegradable zone. KL values (quoted to base e) were greater than
0.50 day-1 compared to 0.39 day-1 for typical municipality
wastewater.
Abstract: In this paper we will develop a sequential life test approach applied to a modified low alloy-high strength steel part used in highway overpasses in Brazil.We will consider two possible underlying sampling distributions: the Normal and theInverse Weibull models. The minimum life will be considered equal to zero. We will use the two underlying models to analyze a fatigue life test situation, comparing the results obtained from both.Since a major chemical component of this low alloy-high strength steel part has been changed, there is little information available about the possible values that the parameters of the corresponding Normal and Inverse Weibull underlying sampling distributions could have. To estimate the shape and the scale parameters of these two sampling models we will use a maximum likelihood approach for censored failure data. We will also develop a truncation mechanism for the Inverse Weibull and Normal models. We will provide rules to truncate a sequential life testing situation making one of the two possible decisions at the moment of truncation; that is, accept or reject the null hypothesis H0. An example will develop the proposed truncated sequential life testing approach for the Inverse Weibull and Normal models.
Abstract: Over the years, there is a growing trend towards
quality-based specifications in highway construction. In many
Quality Control/Quality Assurance (QC/QA) specifications, the
contractor is primarily responsible for quality control of the process,
whereas the highway agency is responsible for testing the acceptance
of the product. A cooperative investigation was conducted in Illinois
over several years to develop a prototype End-Result Specification
(ERS) for asphalt pavement construction. The final characteristics of
the product are stipulated in the ERS and the contractor is given
considerable freedom in achieving those characteristics. The risk for
the contractor or agency depends on how the acceptance limits and
processes are specified. Stochastic simulation models are very useful
in estimating and analyzing payment risk in ERS systems and these
form an integral part of the Illinois-s prototype ERS system. This
paper describes the development of an innovative methodology to
estimate the variability components in in-situ density, air voids and
asphalt content data from ERS projects. The information gained from
this would be crucial in simulating these ERS projects for estimation
and analysis of payment risks associated with asphalt pavement
construction. However, these methods require at least two parties to
conduct tests on all the split samples obtained according to the
sampling scheme prescribed in present ERS implemented in Illinois.
Abstract: A robust control approach is proposed for a high speed manipulator using a hybrid computed torque control approach in the state space. The high-speed manipulator is driven by permanent magnet dc motors to track a trajectory in the joint space in the presence of disturbances. Tracking problem is analyzed in the state space where the completed models are considered for actuators. The proposed control approach can guarantee the stability and a satisfactory tracking performance. A two-link elbow manipulator driven by electrical actuators is simulated and results are shown to satisfy conditions under technical specifications.
Abstract: In this work we present a solution for DAGC (Digital
Automatic Gain Control) in WLAN receivers compatible to IEEE 802.11a/g standard. Those standards define communication in 5/2.4
GHz band using Orthogonal Frequency Division Multiplexing OFDM modulation scheme. WLAN Transceiver that we have used
enables gain control over Low Noise Amplifier (LNA) and a
Variable Gain Amplifier (VGA). The control over those signals is
performed in our digital baseband processor using dedicated hardware block DAGC. DAGC in this process is used to automatically control the VGA and LNA in order to achieve better
signal-to-noise ratio, decrease FER (Frame Error Rate) and hold the
average power of the baseband signal close to the desired set point.
DAGC function in baseband processor is done in few steps: measuring power levels of baseband samples of an RF signal,accumulating the differences between the measured power level and
actual gain setting, adjusting a gain factor of the accumulation, and
applying the adjusted gain factor the baseband values. Based on the measurement results of RSSI signal dependence to input power we have concluded that this digital AGC can be implemented applying
the simple linearization of the RSSI. This solution is very simple but also effective and reduces complexity and power consumption of the
DAGC. This DAGC is implemented and tested both in FPGA and in ASIC as a part of our WLAN baseband processor. Finally, we have integrated this circuit in a compact WLAN PCMCIA board based on MAC and baseband ASIC chips designed from us.
Abstract: This study aims to investigate empirically the valuerelevance
of accounting information to domestic investors in Tehran
stock exchange from 1999 to 2006. During the present research
impacts of two factors, including positive vs. negative earnings and
the firm size are considered as well. The authors used earnings per
share and annual change of earnings per share as the income
statement indices, and book value of equity per share as the balance
sheet index. Return and Price models through regression analysis are
deployed in order to test the research hypothesis. Results depicted
that accounting information is value-relevance to domestic investors
in Tehran Stock Exchange according to both studied models.
However, income statement information has more value-relevance
than the balance sheet information. Furthermore, positive vs. negative
earnings and firm size seems to have significant impact on valuerelevance
of accounting information.
Abstract: Charge Simulation Method (CSM) is one of the very widely used numerical field computation technique in High Voltage (HV) engineering. The high voltage fields of varying non uniformities are encountered in practice. CSM programs being case specific, the simulation accuracies heavily depend on the user (programmers) experience. Here is an effort to understand CSM errors and evolve some guidelines to setup accurate CSM models, relating non uniformities with assignment factors. The results are for the six-point-charge model of sphere-plane gap geometry. Using genetic algorithm (GA) as tool, optimum assignment factors at different non uniformity factors for this model have been evaluated and analyzed. It is shown that the symmetrically placed six-point-charge models can be good enough to set up CSM programs with potential errors less than 0.1% when the field non uniformity factor is greater than 2.64 (field utilization factor less than 52.76%).
Abstract: Single photon detectors have been fabricated NbN
nano wire. These detectors are fabricated from high quality, ultra
high vacuum sputtered NbN thin films on a sapphire substrate. In this
work a typical schematic of the nanowire Single Photon Detector
structure and then driving and measurement electronic circuit are
shown.
The response of superconducting nanowire single photon detectors
during a photo detection event, is modeled by a special electrical
circuits (two circuit).
Finally, current through the wire is calculated by solving
equations of models.
Abstract: This paper presents a simple method for estimation of
additional load as a factor of the existing load that may be drawn
before reaching the point of line maximum loadability of radial
distribution system (RDS) with different realistic load models at
different substation voltages. The proposed method involves a simple
line loadability index (LLI) that gives a measure of the proximity of
the present state of a line in the distribution system. The LLI can use
to assess voltage instability and the line loading margin. The
proposed method also compares with the existing method of
maximum loadability index [10]. The simulation results show that the
LLI can identify not only the weakest line/branch causing system
instability but also the system voltage collapse point when it is near
one. This feature enables us to set an index threshold to monitor and
predict system stability on-line so that a proper action can be taken to
prevent the system from collapse. To demonstrate the validity of the
proposed algorithm, computer simulations are carried out on two bus
and 69 bus RDS.
Abstract: This paper proposes a system to extract images from web pages and then detect the skin color regions of these images. As part of the proposed system, using BandObject control, we built a Tool bar named 'Filter Tool Bar (FTB)' by modifying the Pavel Zolnikov implementation. The Yahoo! Team provides us with the Yahoo! SDK API, which also supports image search and is really useful. In the proposed system, we introduced three new methods for extracting images from the web pages (after loading the web page by using the proposed FTB, before loading the web page physically from the localhost, and before loading the web page from any server). These methods overcome the drawback of the regular expressions method for extracting images suggested by Ilan Assayag. The second part of the proposed system is concerned with the detection of the skin color regions of the extracted images. So, we studied two famous skin color detection techniques. The first technique is based on the RGB color space and the second technique is based on YUV and YIQ color spaces. We modified the second technique to overcome the failure of detecting complex image's background by using the saturation parameter to obtain an accurate skin detection results. The performance evaluation of the efficiency of the proposed system in extracting images before and after loading the web page from localhost or any server in terms of the number of extracted images is presented. Finally, the results of comparing the two skin detection techniques in terms of the number of pixels detected are presented.
Abstract: Recognition of Indian languages scripts is challenging problems. In Optical Character Recognition [OCR], a character or symbol to be recognized can be machine printed or handwritten characters/numerals. There are several approaches that deal with problem of recognition of numerals/character depending on the type of feature extracted and different way of extracting them. This paper proposes a recognition scheme for handwritten Hindi (devnagiri) numerals; most admired one in Indian subcontinent. Our work focused on a technique in feature extraction i.e. global based approach using end-points information, which is extracted from images of isolated numerals. These feature vectors are fed to neuro-memetic model [18] that has been trained to recognize a Hindi numeral. The archetype of system has been tested on varieties of image of numerals. . In proposed scheme data sets are fed to neuro-memetic algorithm, which identifies the rule with highest fitness value of nearly 100 % & template associates with this rule is nothing but identified numerals. Experimentation result shows that recognition rate is 92-97 % compared to other models.
Abstract: Water level forecasting using records of past time series is of importance in water resources engineering and management. For example, water level affects groundwater tables in low-lying coastal areas, as well as hydrological regimes of some coastal rivers. Then, a reliable prediction of sea-level variations is required in coastal engineering and hydrologic studies. During the past two decades, the approaches based on the Genetic Programming (GP) and Artificial Neural Networks (ANN) were developed. In the present study, the GP is used to forecast daily water level variations for a set of time intervals using observed water levels. The measurements from a single tide gauge at Urmia Lake, Northwest Iran, were used to train and validate the GP approach for the period from January 1997 to July 2008. Statistics, the root mean square error and correlation coefficient, are used to verify model by comparing with a corresponding outputs from Artificial Neural Network model. The results show that both these artificial intelligence methodologies are satisfactory and can be considered as alternatives to the conventional harmonic analysis.
Abstract: In this article, the flow behavior around a NACA 0012 airfoil which is oscillating with different Reynolds numbers and in various amplitudes has been investigated numerically. Numerical simulations have been performed with ANSYS software. First, the 2- D geometry has been studied in different Reynolds numbers and angles of attack with various numerical methods in its static condition. This analysis was to choose the best turbulent model and comparing the grids to have the optimum one for dynamic simulations. Because the analysis was to study the blades of wind turbines, the Reynolds numbers were not arbitrary. They were in the range of 9.71e5 to 22.65e5. The angle of attack was in the range of -41.81° to 41.81°. By choosing the forward wind speed as the independent parameter, the others like Reynolds and the amplitude of the oscillation would be known automatically. The results show that the SST turbulent model is the best choice that leads the least numerical error with respect the experimental ones. Also, a dynamic stall phenomenon is more probable at lower wind speeds in which the lift force is less.
Abstract: In neural networks, when new patterns are learned by a network, the new information radically interferes with previously stored patterns. This drawback is called catastrophic forgetting or catastrophic interference. In this paper, we propose a biologically inspired neural network model which overcomes this problem. The proposed model consists of two distinct networks: one is a Hopfield type of chaotic associative memory and the other is a multilayer neural network. We consider that these networks correspond to the hippocampus and the neocortex of the brain, respectively. Information given is firstly stored in the hippocampal network with fast learning algorithm. Then the stored information is recalled by chaotic behavior of each neuron in the hippocampal network. Finally, it is consolidated in the neocortical network by using pseudopatterns. Computer simulation results show that the proposed model has much better ability to avoid catastrophic forgetting in comparison with conventional models.
Abstract: A lot of research has been done in the past decade in the field of audio content analysis for extracting various information from audio signal. One such significant information is the "perceived mood" or the "emotions" related to a music or audio clip. This information is extremely useful in applications like creating or adapting the play-list based on the mood of the listener. This information could also be helpful in better classification of the music database. In this paper we have presented a method to classify music not just based on the meta-data of the audio clip but also include the "mood" factor to help improve the music classification. We propose an automated and efficient way of classifying music samples based on the mood detection from the audio data. We in particular try to classify the music based on mood for Indian bollywood music. The proposed method tries to address the following problem statement: Genre information (usually part of the audio meta-data) alone does not help in better music classification. For example the acoustic version of the song "nothing else matters by Metallica" can be classified as melody music and thereby a person in relaxing or chill out mood might want to listen to this track. But more often than not this track is associated with metal / heavy rock genre and if a listener classified his play-list based on the genre information alone for his current mood, the user shall miss out on listening to this track. Currently methods exist to detect mood in western or similar kind of music. Our paper tries to solve the issue for Indian bollywood music from an Indian cultural context
Abstract: In this paper we propose a segmentation system for unconstrained Arabic online handwriting. An essential problem addressed by analytical-based word recognition system. The system is composed of two-stages the first is a newly special designed hidden Markov model (HMM) and the second is a rules based stage. In our system, handwritten words are broken up into characters by simultaneous segmentation-recognition using HMMs of unique design trained using online features most of which are novel. The HMM output characters boundaries represent the proposed segmentation points (PSP) which are then validated by rules-based post stage without any contextual information help to solve different segmentation errors. The HMM has been designed and tested using a self collected dataset (OHASD) [1]. Most errors cases are cured and remarkable segmentation enhancement is achieved. Very promising word and character segmentation rates are obtained regarding the unconstrained Arabic handwriting difficulty and not using context help.
Abstract: Exposure to ambient air pollution has been linked to a
number of health outcomes, starting from modest transient changes in
the respiratory tract and impaired pulmonary function, continuing to
restrict activity/reduce performance and to the increase emergency
rooms visits, hospital admissions or mortality. The increase of
allergenic symptoms has been associated with air contaminants such
as ozone, particulate matter, fungal spores and pollen.
Considering the potential relevance of crossed effects of nonbiological
pollutants and airborne pollens and fungal spores on
allergy worsening, the aim of this work was to evaluate the influence
of non-biological pollutants (O3 and PM10) and meteorological
parameters on the concentrations of pollen and fungal spores using
multiple linear regressions.
The data considered in this study were collected in Oporto which
is the second largest Portuguese city, located in the North. Daily
mean of O3, PM10, pollen and fungal spore concentrations,
temperature, relative humidity, precipitation, wind velocity, pollen
and fungal spore concentrations, for 2003, 2004 and 2005 were
considered. Results showed that the 90th percentile of the adjusted
coefficient of determination, P90 (R2aj), of the multiple regressions
varied from 0.613 to 0.916 for pollen and from 0.275 to 0.512 for
fungal spores. O3 and PM10 showed to have some influence on the
biological pollutants. Among the meteorological parameters
analysed, temperature was the one that most influenced the pollen
and fungal spores airborne concentrations. Relative humidity also
showed to have some influence on the fungal spore dispersion.
Nevertheless, the models for each pollen and fungal spore were
different depending on the analysed period, which means that the
correlations identified as statistically significant can not be, even so,
consistent enough.
Abstract: Accurate modeling of high speed RLC interconnects
has become a necessity to address signal integrity issues in current
VLSI design. To accurately model a dispersive system of interconnects
at higher frequencies; a full-wave analysis is required.
However, conventional circuit simulation of interconnects with full
wave models is extremely CPU expensive. We present an algorithm
for reducing large VLSI circuits to much smaller ones with similar
input-output behavior. A key feature of our method, called Frequency
Shift Technique, is that it is capable of reducing linear time-varying
systems. This enables it to capture frequency-translation and sampling
behavior, important in communication subsystems such as mixers,
RF components and switched-capacitor filters. Reduction is obtained
by projecting the original system described by linear differential
equations into a lower dimension. Experiments have been carried out
using Cadence Design Simulator cwhich indicates that the proposed
technique achieves more % reduction with less CPU time than the
other model order reduction techniques existing in literature. We
also present applications to RF circuit subsystems, obtaining size
reductions and evaluation speedups of orders of magnitude with
insignificant loss of accuracy.
Abstract: This paper describes a new method of unequal error
protection (UEP) for region of interest (ROI) with embedded zerotree
wavelet algorithm (EZW). ROI technique is important in applications
with different parts of importance. In ROI coding, a chosen ROI is
encoded with higher quality than the background (BG). Unequal
error protection of image is provided by different coding techniques.
In our proposed method, image is divided into two parts (ROI, BG)
that consist of more important bytes (MIB) and less important bytes
(LIB). The experimental results verify effectiveness of the design.
The results of our method demonstrate the comparison of the unequal
error protection (UEP) of image transmission with defined ROI and
the equal error protection (EEP) over multiple noisy channels.
Abstract: Depression is a serious mental health problem that
affects people of all ages, including children and adolescents. Studies
showed that female gender is one of the risk factors may influence
the development of depression in adolescents. However, some of the
studies from Turkey suggested that gender does not lead to any
significant difference in the youth depression level. Therefore, the
presented study investigated whether girls differ from boys in respect
of depression. The association between genders and test scores for
the adolescents in a population of primary and secondary school
students was also evaluated. The study was consisting of 254
adolescents (122 boys and 132 girls) with a mean age of 13.86±1.43
(Mean±SD) ranging from 12-16 years. Psychological assessment was
performed using Children-s Depression Inventory (CDI). Chi-square
and Student-s t-test statistics were employed to analyze the data. The
mean of the CDI scores of the girls were higher than boys- CDI
scores (t = -4.580, p = 0.001). Higher ratio appeared for the girls
when they compared with boy group-s depression levels using a CDI
cut-off point of 19 (p = 0.001, Odds Ratio = 2,603). The findings of
the present study suggested that adolescent girls have high level of
depression than adolescent boys aged between 12-16 years in
Turkey. Although some studies reported that there is no any
differences depression level between adolescent boys and girls in
Turkey, result of the present study showed that adolescent girls have
high level of depression than adolescent boys in Turkey.